Estimating the impact of physician risky-prescribing on the network structure underlying physician shared-patient relationships.

Social network analysis and shared-patient physician networks have become effective ways of studying physician collaborations. Assortative mixing or "homophily" is the network phenomenon whereby the propensity for similar individuals to form ties is greater than for dissimilar individuals. Motivated by the public health concern of risky-prescribing among older patients in the United States, we develop network models and tests involving novel network measures to study whether there is evidence of geographic homophily in prescribing and deprescribing in the specific shared-patient network of physicians linked to the US state of Ohio in 2014. Evidence of homophily in risky-prescribing would imply that prescribing behaviors help shape physician networks and could inform interventions to reduce risky-prescribing (e.g., should interventions target groups of physicians or select physicians at random). Furthermore, if such effects varied depending on the structural features of a physician's position in the network (e.g., by whether or not they are involved in cliques - groups of actors that are fully connected to each other - such as closed triangles in the case of three actors), this would further strengthen the case for targeting of select physicians for interventions. Using accompanying Medicare Part D data, we converted patient longitudinal prescription receipts into novel measures of the intensity of each physician's risky-prescribing. Exponential random graph models were used to simultaneously estimate the importance of homophily in prescribing and deprescribing in the network beyond the characteristics of physician specialty (or other metadata) and network-derived features. In addition, novel network measures were introduced to allow homophily to be characterized in relation to specific triadic (three-actor) structural configurations in the network with associated non-parametric randomization tests to evaluate their statistical significance in the network against the null hypothesis of no such phenomena. We found physician homophily in prescribing and deprescribing in both the state-wide and multiple HRR sub-networks, and that the level of homophily varied across HRRs. We also found that physicians exhibited within-triad homophily in risky-prescribing, with the prevalence of homophilic triads significantly higher than expected by chance absent homophily. These results may explain why communities of prescribers emerge and evolve, helping to justify group-level prescriber interventions. The methodology could be applied to arbitrary shared-patient networks and even more generally to other kinds of network data that underlies other kinds of social phenomena.

The Association Between Oncology Outreach and Timely Treatment for Rural Patients with Breast Cancer: A Claims-Based Approach.

BACKGROUND: Oncology outreach is a common strategy for increasing rural access to cancer care, where traveling oncologists commute across healthcare settings to extend specialized care. Examining the extent to which physician outreach is associated with timely treatment for rural patients is critical for informing outreach strategies. METHODS: We identified a 100% fee-for-service sample of incident breast cancer patients from 2015 to 2020 Medicare claims and apportioned them into surgery and adjuvant therapy cohorts based on treatment history. We defined an outreach visit as the provision of care by a traveling oncologist at a clinic outside of their primary hospital service area. We used hierarchical logistic regression to examine the associations between patient receipt of preoperative care at an outreach visit (preoperative outreach) and > 60-day surgical delay, and patient receipt of postoperative care at an outreach visit (postoperative outreach) and > 60-day adjuvant delay. RESULTS: We identified 30,337 rural-residing patients who received breast cancer surgery, of whom 4071 (13.4%) experienced surgical delay. Among surgical patients, 14,501 received adjuvant therapy, of whom 2943 (20.3%) experienced adjuvant delay. In adjusted analysis, we found that patient receipt of preoperative outreach was associated with reduced odds of surgical delay (odds ratio [OR] 0.75, 95% confidence interval [CI] 0.61-0.91); however, we found no association between patient receipt of postoperative outreach and adjuvant delay (OR 1.04, 95% CI 0.85-1.25). CONCLUSIONS: Our findings indicate that preoperative outreach is protective against surgical delay. The traveling oncologists who enable such outreach may play an integral role in catalyzing the coordination and timeliness of patient-centered care.

Disparities in Access to Multidisciplinary Cancer Consultations and Treatment for Patients With Early-Stage Non-Small Cell Lung Cancer: A SEER-Medicare Analysis.

PURPOSE: Disparities in access to a multidisciplinary cancer consultation (MDCc) persist, and the role of physician relationships remains understudied. This study examined the extent to which multilevel factors, including patient characteristics and patient-sharing network measures reflecting the structure of physician relationships, are associated with an MDCc and receipt of stereotactic body radiation therapy versus surgery among patients with early-stage non-small cell lung cancer (NSCLC). METHODS AND MATERIALS: In this cross-sectional study, we analyzed Surveillance, Epidemiology, and End Results (SEER)-Medicare data for patients diagnosed with stage I-IIA NSCLC from 2016 to 2017. We assembled patient-sharing networks and identified cancer specialists who were locally unique for their specialty, herein referred to as linchpins. The proportion of linchpin cancer specialists for each hospital referral region (HRR) was calculated as a network-based measure of specialist scarcity. We used multilevel multinomial logistic regression to estimate associations between study variables and receipt of an MDCc and multilevel logistic regression to examine the relationship between patient receipt of an MDCc and initial treatment. RESULTS: Our study included 6120 patients with stage I-IIA NSCLC, of whom 751 (12.3%) received an MDCc, 1729 (28.3%) consulted only a radiation oncologist, 2010 (32.8%) consulted only a surgeon, and 1630 (26.6%) consulted neither specialist within 2 months of diagnosis. Compared with patients residing in an HRR with a low proportion of linchpin surgeons, those residing in an HRR with a high proportion of linchpin surgeons had a 2.99 (95% CI, 1.87-4.78) greater relative risk of consulting only a radiation oncologist versus receiving an MDCc and a 2.70 (95% CI, 1.68-4.35) greater relative risk of consulting neither specialist versus receiving an MDCc. Patients who received an MDCc were 5.32 times (95% CI, 4.27-6.63) more likely to receive stereotactic body radiation therapy versus surgery. CONCLUSIONS: Physician networks are associated with receipt of an MDCc and treatment, underscoring the potential for leveraging patient-sharing network analysis to improve access to lung cancer care.

Rural-urban disparities in health care delivery for children with medical complexity and moderating effects of payer, disability, and community poverty.

PURPOSE: Children with medical complexity (CMC) may be at increased risk of rural-urban disparities in health care delivery given their multifaceted health care needs, but these disparities are poorly understood. This study evaluated rural-urban disparities in health care delivery to CMC and determined whether Medicaid coverage, co-occurring disability, and community poverty modified the effects of rurality on care delivery. METHODS: This retrospective cohort study of 2012-2017 all-payer claims data from Colorado, Massachusetts, and New Hampshire included CMC <18 years. Health care delivery measures (ambulatory clinic visits, emergency department visits, acute care hospitalizations, total hospital days, and receipt of post-acute care) were compared for rural- versus urban-residing CMC in multivariable regression models, following established methods to evaluate effect modification. FINDINGS: Of 112,475 CMC, 7307 (6.5%) were rural residing and 105,168 (93.5%) were urban residing. A total of 68.9% had Medicaid coverage, 33.9% had a disability, and 39.7% lived in communities with >20% child poverty. In adjusted analyses, rural-residing CMC received significantly fewer ambulatory visits (risk ratio [RR] = 0.95, 95% confidence interval [CI]: 0.94-0.96), more emergency visits (RR = 1.12, 95% CI: 1.08-1.16), and fewer hospitalization days (RR = 0.90, 95% CI = 0.85-0.96). The estimated modification effects of rural residence by Medicaid coverage, disability, and community poverty were each statistically significant. Differences in the odds of having a hospitalization and receiving post-acute care did not persist after incorporating sociodemographic and clinical characteristics and interaction effects. CONCLUSIONS: Rural- and urban-residing CMC differed in their receipt of health care, and Medicaid coverage, co-occurring disabilities, and community poverty modified several of these effects. These modifying effects should be considered in clinical and policy initiatives to ensure that such initiatives do not widen rural-urban disparities.

Characterizing the Traveling Oncology Workforce and Its Influence on Patient Travel Burden: A Claims-Based Approach.

PURPOSE: Oncology outreach is a common strategy for extending cancer care to rural patients. However, a nationwide characterization of the traveling workforce that enables this outreach is lacking, and the extent to which outreach reduces travel burden for rural patients is unknown. METHODS: This cross-sectional study analyzed a rural (nonurban) subset of a 100% fee-for-service sample of 355,139 Medicare beneficiaries with incident breast, colorectal, and lung cancers. Surgical, medical, and radiation oncologists were linked to patients using Part B claims, and traveling oncologists were identified by observing hospital service area (HSA) transition patterns. We defined oncology outreach as the provision of cancer care by a traveling oncologist outside of their primary HSA. We used hierarchical gamma regression models to examine the separate associations between patient receipt of oncology outreach and one-way patient travel times to chemotherapy, radiotherapy, and surgery. RESULTS: On average, 9,935 of 39,960 oncologists conducted annual outreach, where 57.8% traveled with low frequency (0-1 outreach visits/mo), 21.1% with medium frequency (1-3 outreach visits/mo), and 21.1% with high frequency (>3 outreach visits/mo). Oncologists provided surgery, radiotherapy, and chemotherapy to 51,715, 27,120, and 5,874 rural beneficiaries, respectively, of whom 2.5%, 6.9%, and 3.6% received oncology outreach. Rural patients who received oncology outreach traveled 16% (95% CI, 11 to 21) and 11% (95% CI, 9 to 13) less minutes to chemotherapy and radiotherapy than those who did not receive oncology outreach, corresponding to expected one-way savings of 15.9 (95% CI, 15.5 to 16.4) and 11.9 (95% CI, 11.7 to 12.2) minutes, respectively. CONCLUSION: Our study introduces a novel claims-based approach for tracking the nationwide traveling oncology workforce and supports oncology outreach as an effective means for improving rural access to cancer care.

Comparison of US Oncologist Rurality by Practice Setting and Patients Served.

Importance: Studies of the oncology workforce most often classify physician rurality by their practice location, but this could miss the true extent of physicians involved in rural cancer care. Objective: To compare a method for identifying oncology physicians involved in rural cancer care that uses the proportion of rural patients served with the standard method based on practice location. Design, Setting, and Participants: This cross-sectional study used retrospective Centers for Medicare & Medicaid Services encounter data on medical oncologists, radiation oncologists, and surgeons treating Medicare beneficiaries diagnosed with breast, colorectal, or lung cancer from January 1 to December 31, 2019. Data were analyzed from May to September 2023. Main Outcomes and Measures: The standard method of classifying oncologist physician rurality based on practice location was compared with a novel method of classification based on proportion of rural patients served. Results: The study included 27 870 oncology physicians (71.3% male), of whom 835 (3.0%) practiced in a rural location. Physicians practicing in a rural location treated a high proportion of rural patients (median, 50.0% [IQR, 16.7%-100%]). When considering the rurality of physicians' patient panels, 5123 physicians (18.4%) whose patient panel included at least 20% rural patients, 3199 (11.5%) with at least 33% rural patients, and 1996 (7.2%) with at least 50% rural patients were identified. Using a physician's patient panel to classify physician rurality revealed a higher number and greater spread of oncology physicians involved in rural cancer care in the US than the standard method, while maintaining high performance (area under the curve, 0.857) and fair concordance (κ, 0.346; 95% CI, 0.323-0.369) with the method based on practice setting. Conclusions and Relevance: In this cross-sectional study, classifying oncologist rurality by the proportion of rural patients served identified more oncology physicians treating patients living in rural areas than the standard method of practice location and may more accurately capture the rural cancer physician workforce, as many hospitals have historically been located in more urban areas. This new method may be used to improve future studies of rural cancer care delivery.

Dissecting patterns and predictors of interhospital transfers for patients with brain metastasis.

OBJECTIVE: Interhospital transfers in the acute setting may contribute to high cost, patient inconvenience, and delayed treatment. The authors sought to understand patterns and predictors in the transfer of brain metastasis patients after emergency department (ED) encounter. METHODS: The authors analyzed 3037 patients with brain metastasis who presented to the ED in Massachusetts and were included in the Healthcare Cost and Utilization Project State Inpatient Database and State Emergency Department Database in 2018 and 2019. RESULTS: The authors found that 6.9% of brain metastasis patients who presented to the ED were transferred to another facility, either directly or indirectly after admission. The sending EDs were more likely to be nonteaching hospitals without neurosurgery and radiation oncology services (p < 0.01). Transferred patients were more likely to present with neurological symptoms compared to those admitted or discharged (p < 0.01). Among those transferred, approximately 30% did not undergo a significant procedure after transfer and approximately 10% were discharged within 3 days, in addition to not undergoing significant interventions. In total, 74% of transferred patients were sent to a facility significantly farther (> 3 miles) than the nearest facility with neurosurgery and radiation oncology services. Further distance transfers were not associated with improvements in 30-day readmission rate (OR [95% CI] 0.64 [0.30-1.34] for 15-30 miles; OR [95% CI] 0.73 [0.37-1.46] for > 30 miles), 90-day readmission rate (OR [95% CI] 0.50 [0.18-1.28] for 15-30 miles; OR [95% CI] 0.53 [0.18-1.51] for > 30 miles), and length of stay (OR [95% CI] 1.21 days [0.94-1.29] for both 15-30 miles and > 30 miles) compared to close-distance transfers. CONCLUSIONS: The authors identified a notable proportion of transfers without subsequent significant intervention or appreciable medical management. This may reflect ED physician discomfort with the neurological symptoms of brain metastasis. Many patients were also transferred to hospitals distant from their point of origin and demonstrated no differences in readmission rates and length of stay.

Broadband Data Forensics: Spatiotemporal Variations of the Download/Upload Speed Metric Commonly Used to Evaluate Potential Telehealth Accessibility.

Introduction: The complicated task of evaluating potential telehealth access begins with the metrics and supporting datasets that seek toevaluate the presence and durability of broadband connections in a community. Broadband download/upload speeds are one of the popular metrics used to measure potential telehealth access, which is critical to health equity. An understanding of the limitations of these measures is important for drawing conclusions about the reality of the digital divide in telehealth access. The objective of this study was to assess spatiotemporal variations in broadband download/upload speeds. Method: We analyzed a sample of data from the Speedtest Intelligence Portal provided through the Ookla for Good initiative. Results: We found that variation is inherent across the states of Vermont, New Hampshire, Louisiana, and Utah. Conclusions: The variation suggests that when single measures of download/upload speeds are used to evaluate telehealth accessibility they may be masking the true magnitude of the digital divide.

Association between a network-based physician linchpin score and cancer patient mortality: a SEER-Medicare analysis.

BACKGROUND: Patients with cancer frequently require multidisciplinary teams for optimal cancer outcomes. Network analysis can capture relationships among cancer specialists, and we developed a novel physician linchpin score to characterize "linchpin" physicians whose peers have fewer ties to other physicians of the same oncologic specialty. Our study examined whether being treated by a linchpin physician was associated with worse survival. METHODS: In this cross-sectional study, we analyzed Surveillance, Epidemiology, and End Results-Medicare data for patients diagnosed with stage I to III non-small cell lung cancer or colorectal cancer (CRC) in 2016-2017. We assembled patient-sharing networks and calculated linchpin scores for medical oncologists, radiation oncologists, and surgeons. Physicians were considered linchpins if their linchpin score was within the top 15% for their specialty. We used Cox proportional hazards models to examine associations between being treated by a linchpin physician and survival, with a 2-year follow-up period. RESULTS: The study cohort included 10 081 patients with non-small cell lung cancer and 9036 patients with CRC. Patients with lung cancer treated by a linchpin radiation oncologist had a 17% (95% confidence interval = 1.04 to 1.32) greater hazard of mortality, and similar trends were observed for linchpin medical oncologists. Patients with CRC treated by a linchpin surgeon had a 22% (95% confidence interval = 1.03 to 1.43) greater hazard of mortality. CONCLUSIONS: In an analysis of Medicare beneficiaries with nonmetastatic lung cancer or CRC, those treated by linchpin physicians often experienced worse survival. Efforts to improve outcomes can use network analysis to identify areas with reduced access to multidisciplinary specialists.

Breaking membrane barriers to neutralize E. coli and K. pneumoniae virulence with PEGylated branched polyethylenimine.

Bacterial infections caused by Gram-negative pathogens, such as those in the family Enterobacteriaceae, are among the most difficult to treat because effective therapeutic options are either very limited or non-existent. This raises serious concern regarding the emergence and spread of multi-drug resistant (MDR) pathogens in the community setting; and thus, creates the need for discovery efforts and/or early-stage development of novel therapies for infections. Our work is directed towards branched polyethylenimine (BPEI) modified with polyethylene glycol (PEG) as a strategy for targeting virulence from Gram-negative bacterial pathogens. Here, we neutralize lipopolysaccharide (LPS) as a barrier to the influx of antibiotics. Data demonstrate that the ß-lactam antibiotic oxacillin, generally regarded as ineffective against Gram-negative bacteria, can be potentiated by 600 Da BPEI to kill some Escherichia coli and some Klebsiella pneumoniae. Modification of 600 Da BPEI with polyethylene glycol (PEG) could increase drug safety and improves potentiation activity. The ability to use the Gram-positive agent, oxacillin, against Gram-negative pathogens could expand the capability to deliver effective treatments that simplify, reduce, or eliminate some complicated treatment regimens.

Telehealth Use Following COVID-19 Within Patient-Sharing Physician Networks at a Rural Comprehensive Cancer Center: Cross-sectional Analysis.

BACKGROUND: In response to the COVID-19 pandemic, cancer centers rapidly adopted telehealth to deliver care remotely. Telehealth will likely remain a model of care for years to come and may not only affect the way oncologists deliver care to their own patients but also the physicians with whom they share patients. OBJECTIVE: This study aimed to examine oncologist characteristics associated with telehealth use and compare patient-sharing networks before and after the COVID-19 pandemic in a rural catchment area with a particular focus on the ties between physicians at the comprehensive cancer center and regional facilities. METHODS: In this retrospective observational study, we obtained deidentified electronic health record data for individuals diagnosed with breast, colorectal, or lung cancer at Dartmouth Health in New Hampshire from 2018-2020. Hierarchical logistic regression was used to identify physician factors associated with telehealth encounters post COVID-19. Patient-sharing networks for each cancer type before and post COVID-19 were characterized with global network measures. Exponential-family random graph models were performed to estimate homophily terms for the likelihood of ties existing between physicians colocated at the hub comprehensive cancer center. RESULTS: Of the 12,559 encounters between patients and oncologists post COVID-19, 1228 (9.8%) were via telehealth. Patient encounters with breast oncologists who practiced at the hub hospital were over twice as likely to occur via telehealth compared to encounters with oncologists who practiced in regional facilities (odds ratio 2.2, 95% CI 1.17-4.15; P=.01). Patient encounters with oncologists who practiced in multiple locations were less likely to occur via telehealth, and this association was statistically significant for lung cancer care (odds ratio 0.26, 95% CI 0.09-0.76; P=.01). We observed an increase in ties between oncologists at the hub hospital and oncologists at regional facilities in the lung cancer network post COVID-19 compared to before COVID-19 (93/318, 29.3%, vs 79/370, 21.6%, respectively), which was also reflected in the lower homophily coefficients post COVID-19 compared to before COVID-19 for physicians being colocated at the hub hospital (estimate: 1.92, 95% CI 1.46-2.51, vs 2.45, 95% CI 1.98-3.02). There were no significant differences observed in breast cancer or colorectal cancer networks. CONCLUSIONS: Telehealth use and associated changes to patient-sharing patterns associated with telehealth varied by cancer type, suggesting disparate approaches for integrating telehealth across clinical groups within this health system. The limited changes to the patient-sharing patterns between oncologists at the hub hospital and regional facilities suggest that telehealth was less likely to create new referral patterns between these types of facilities and rather replace care that would otherwise have been delivered in person. However, this study was limited to the 2 years immediately following the initial outbreak of COVID-19, and longer-term follow-up may uncover delayed effects that were not observed in this study period.

The interaction of rurality and rare cancers for travel time to cancer care.

PURPOSE: Geographic access to cancer care is known to significantly impact utilization and outcomes. Longer travel times have negative impacts for patients requiring highly specialized care, such as for rare cancers, and for those in rural areas. Scant population-based research informs geographic access to care for rare cancers and whether rurality impacts that access. METHODS: Using Medicare data (2014-2015), we identified prevalent cancers and cancer-directed surgeries, chemotherapy, and radiation. We classified cancers as rare (incidence <6/100,000/year) or common (incidence ≥6/100,000/year) using previously published thresholds and categorized rurality from ZIP code of beneficiary residence. We estimated travel time between beneficiaries and providers for each service based on ZIP code. Descriptive statistics summarized travel time by rare versus common cancers, service type, and rurality. FINDINGS: We included 1,169,761 Medicare beneficiaries (21.9% in nonmetropolitan areas), 87,399; 7.5% had rare cancers, with 9,133,003 cancer-directed services. Travel times for cancer services ranged from approximately 29 minutes (25th percentile) to 68 minutes (75th percentile). Travel times were similar for rare and common cancers overall (median: 45 vs 43 minutes) but differed by service type; 13.4% of surgeries were >2 hours away for rare cancers, compared to 8.3% for common cancers. Increasing rurality disproportionately increased travel time to surgical care for rare compared to common cancers. CONCLUSIONS: Travel times to cancer services are longest for surgery, especially among rural residents, yet not markedly longer overall between rare versus common cancers. Understanding geographic access to cancer care for patients with rare cancers is important to delivering specialized care.

Use of a Novel Network-Based Linchpin Score to Characterize Accessibility to the Oncology Physician Workforce in the United States.

Importance: Physician headcounts provide useful information about the cancer care delivery workforce; however, efforts to track the oncology workforce would benefit from new measures that capture how essential a physician is for meeting the multidisciplinary cancer care needs of the region. Physicians are considered linchpins when fewer of their peers are connected to other physicians of the same specialty as the focal physician. Because they are locally unique for their specialty, these physicians' networks may be particularly vulnerable to their removal from the network (eg, through relocation or retirement). Objective: To examine a novel network-based physician linchpin score within nationwide cancer patient-sharing networks and explore variation in network vulnerability across hospital referral regions (HRRs). Design, Setting, and Participants: This cross-sectional study analyzed fee-for-service Medicare claims and included Medicare beneficiaries with an incident diagnosis of breast, colorectal, or lung cancer from 2016 to 2018 and their treating physicians. Data were analyzed from March 2022 to October 2022. Exposures: Physician characteristics assessed were specialty, rurality, and Census region. HRR variables assessed include sociodemographic and socioeconomic characteristics and use of cancer services. Main Outcomes and Measures: Oncologist linchpin score, which examined the extent to which a physician's peers were connected to other physicians of the same specialty as the focal physician. Network vulnerability, which distinguished HRRs with more linchpin oncologists than expected based on oncologist density. χ2 and Fisher exact tests were used to examine relationships between oncologist characteristics and linchpin score. Spearman rank correlation coefficient (ρ) was used to measure the strength and direction of relationships between HRR network vulnerability, oncologist density, population sociodemographic and socioeconomic characteristics, and cancer service use. Results: The study cohort comprised 308 714 patients with breast, colorectal, or lung cancer. The study cohort of 308 714 patients included 161 206 (52.2%) patients with breast cancer, 76 604 (24.8%) patients with colorectal cancer, and 70 904 (23.0%) patients with lung cancer. In our sample, 272 425 patients (88%) were White, and 238 603 patients (77%) lived in metropolitan areas. The cancer patient-sharing network included 7221 medical oncologists and 3573 radiation oncologists. HRRs with more vulnerable networks for medical oncology had a higher percentage of beneficiaries eligible for Medicaid (ρ, 0.19; 95% CI, 0.08 to 0.29). HRRs with more vulnerable networks for radiation oncology had a higher percentage of beneficiaries living in poverty (ρ, 0.17; 95% CI, 0.06 to 0.27), and a higher percentage of beneficiaries eligible for Medicaid (ρ, 0.21; 95% CI, 0.09 to 0.31), and lower rates of cohort patients receiving radiation therapy (ρ, -0.18; 95% CI, -0.28 to -0.06; P = .003). The was no association between network vulnerability for medical oncology and percent of cohort patients receiving chemotherapy (ρ, -0.03; 95% CI, -0.15 to 0.08). Conclusions and Relevance: This study found that patient-sharing network vulnerability was associated with poverty and lower rates of radiation therapy. Health policy strategies for addressing network vulnerability may improve access to interdisciplinary care and reduce treatment disparities.

Using passive sensor data to probe associations of social structure with changes in personality: A synthesis of network analysis and machine learning.

Social network analysis (SNA) is an increasingly popular and effective tool for modeling psychological phenomena. Through application to the personality literature, social networks, in conjunction with passive, non-invasive sensing technologies, have begun to offer powerful insight into personality state variability. Resultant constructions of social networks can be utilized alongside machine learning-based frameworks to uniquely model personality states. Accordingly, this work leverages data from a previously published study to combine passively collected wearable sensor information on face-to-face, workplace social interactions with ecological momentary assessments of personality state. Data from 54 individuals across six weeks was used to explore the relative importance of 26 unique structural and nodal social network features in predicting individual changes in each of the Big Five (5F) personality states. Changes in personality state were operationalized by calculating the weekly root mean square of successive differences (RMSSD) in 5F state scores measured daily via self-report. Using only SNA-derived features from wearable sensor data, boosted tree-based machine learning models explained, on average, approximately 28-30% of the variance in individual personality state change. Model introspection implicated egocentric features as the most influential predictors across 5F-specific models, with network efficiency, constraint, and effective size measures among the most important. Feature importance profiles for each 5F model partially echoed previous empirical findings. Results support future efforts focusing on egocentric components of SNA and suggest particular investment in exploring efficiency measures to model personality fluctuations within the workplace setting.

Rural-Urban Differences in Breast Cancer Surgical Delays in Medicare Beneficiaries.

BACKGROUND: Delays between breast cancer diagnosis and surgery are associated with worsened survival. Delays are more common in urban-residing patients, although factors specific to surgical delays among rural and urban patients are not well understood. METHODS: We used a 100% sample of fee-for-service Medicare claims during 2007-2014 to identify 238,491 women diagnosed with early-stage breast cancer undergoing initial surgery and assessed whether they experienced biopsy-to-surgery intervals > 90 days. We employed multilevel regression to identify associations between delays and patient, regional, and surgeon characteristics, both in combined analyses and stratified by rurality of patient residence. RESULTS: Delays were more prevalent among urban patients (2.5%) than rural patients (1.9%). Rural patients with medium- or high-volume surgeons had lower odds of delay than patients with low-volume surgeons (odds ratio [OR] = 0.71, 95% confidence interval [CI] = 0.58-0.88; OR = 0.74, 95% CI = 0.61-0.90). Rural patients whose surgeon operated at ≥ 3 hospitals were more likely to experience delays (OR = 1.29, 95% CI = 1.01-1.64, Ref: 1 hospital). Patient driving times ≥ 1 h were associated with delays among urban patients only. Age, black race, Hispanic ethnicity, multimorbidity, and academic/specialty hospital status were associated with delays. CONCLUSIONS: Sociodemographic, geographic, surgeon, and facility factors have distinct associations with > 90-day delays to initial breast cancer surgery. Interventions to improve timeliness of breast cancer surgery may have disparate impacts on vulnerable populations by rural-urban status.

Prevalence of Children With Medical Complexity and Associations With Health Care Utilization and In-Hospital Mortality.

Importance: Children with medical complexity (CMC) have substantial health care needs and frequently experience poor health care quality. Understanding the population prevalence and associated health care needs can inform clinical and public health initiatives. Objective: To estimate the prevalence of CMC using open-source pediatric algorithms, evaluate performance of these algorithms in predicting health care utilization and in-hospital mortality, and identify associations between medical complexity as defined by these algorithms and clinical outcomes. Design, Setting, and Participants: This retrospective cohort study used all-payer claims data from Colorado, Massachusetts, and New Hampshire from 2012 through 2017. Children and adolescents younger than 18 years residing in these states were included if they had 12 months or longer of enrollment in a participating health care plan. Analyses were conducted from March 12, 2021, to January 7, 2022. Exposures: The pediatric Complex Chronic Condition Classification System, Pediatric Medical Complexity Algorithm, and Children With Disabilities Algorithm were applied to 3 years of data to identify children with complex and disabling conditions, first in their original form and then using more conservative criteria that required multiple health care claims or involvement of 3 or more body systems. Main Outcomes and Measures: Primary outcomes, examined over 2 years, included in-hospital mortality and a composite measure of health care services, including specialized therapies, specialized medical equipment, and inpatient care. Outcomes were modeled using logistic regression. Model performance was evaluated using C statistics, sensitivity, and specificity. Results: Of 1 936 957 children, 48.4% were female, 87.8% resided in urban core areas, and 45.1% had government-sponsored insurance as their only primary payer. Depending on the algorithm and coding criteria applied, 0.67% to 11.44% were identified as CMC. All 3 algorithms had adequate discriminative ability, sensitivity, and specificity to predict in-hospital mortality and composite health care services (C statistic = 0.76 [95% CI, 0.73-0.80] to 0.81 [95% CI, 0.78-0.84] for mortality and 0.77 [95% CI, 0.76-0.77] to 0.80 [95% CI, 0.79-0.80] for composite health care services). Across algorithms, CMC had significantly greater odds of mortality (adjusted odds ratio [aOR], 9.97; 95% CI, 7.70-12.89; to aOR, 69.35; 95% CI, 52.52-91.57) and composite health care services (aOR, 4.59; 95% CI, 4.44-4.73; to aOR, 18.87; 95% CI, 17.87-19.93) than children not identified as CMC. Conclusions and Relevance: In this study, open-source algorithms identified different cohorts of CMC in terms of prevalence and magnitude of risk, but all predicted increased health care utilization and in-hospital mortality. These results can inform research, programs, and policies for CMC.

A measure of local uniqueness to identify linchpins in a social network with node attributes.

Network centrality measures assign importance to influential or key nodes in a network based on the topological structure of the underlying adjacency matrix. In this work, we define the importance of a node in a network as being dependent on whether it is the only one of its kind among its neighbors' ties. We introduce linchpin score, a measure of local uniqueness used to identify important nodes by assessing both network structure and a node attribute. We explore linchpin score by attribute type and examine relationships between linchpin score and other established network centrality measures (degree, betweenness, closeness, and eigenvector centrality). To assess the utility of this measure in a real-world application, we measured the linchpin score of physicians in patient-sharing networks to identify and characterize important physicians based on being locally unique for their specialty. We hypothesized that linchpin score would identify indispensable physicians who would not be easily replaced by another physician of their specialty type if they were to be removed from the network. We explored differences in rural and urban physicians by linchpin score compared with other network centrality measures in patient-sharing networks representing the 306 hospital referral regions in the United States. We show that linchpin score is uniquely able to make the distinction that rural specialists, but not rural general practitioners, are indispensable for rural patient care. Linchpin score reveals a novel aspect of network importance that can provide important insight into the vulnerability of health care provider networks. More broadly, applications of linchpin score may be relevant for the analysis of social networks where interdisciplinary collaboration is important.

MethylSPWNet and MethylCapsNet: Biologically Motivated Organization of DNAm Neural Networks, Inspired by Capsule Networks.

DNA methylation (DNAm) alterations have been heavily implicated in carcinogenesis and the pathophysiology of diseases through upstream regulation of gene expression. DNAm deep-learning approaches are able to capture features associated with aging, cell type, and disease progression, but lack incorporation of prior biological knowledge. Here, we present modular, user-friendly deep-learning methodology and software, MethylCapsNet and MethylSPWNet, that group CpGs into biologically relevant capsules-such as gene promoter context, CpG island relationship, or user-defined groupings-and relate them to diagnostic and prognostic outcomes. We demonstrate these models' utility on 3,897 individuals in the classification of central nervous system (CNS) tumors. MethylCapsNet and MethylSPWNet provide an opportunity to increase DNAm deep-learning analyses' interpretability by enabling a flexible organization of DNAm data into biologically relevant capsules.

Dual-Function Potentiation by PEG-BPEI Restores Activity of Carbapenems and Penicillins against Carbapenem-Resistant Enterobacteriaceae.

The rise of life-threatening carbapenem-resistant Enterobacteriaceae (CRE) infections has become a critical medical threat. Some of the most dangerous CRE bacteria can produce enzymes that degrade a wide range of antibiotics, including carbapenems and ß-lactams. Infections by CRE have a high mortality rate, and survivors can have severe morbidity from treatment with toxic last-resort antibiotics. CRE have mobile genetic elements that transfer resistance genes to other species. These bacteria also circulate throughout the healthcare system. The mobility and spread of CRE need to be curtailed, but these goals are impeded by having few agents that target a limited range of pathogenic CRE species. Against CRE possessing the metallo-ß-lactamase NDM-1, Klebsiella pneumoniae ATCC BAA-2146 and Escherichia coli ATCC BAA-2452, the potentiation of meropenem and imipenem is possible with low-molecular weight branched polyethylenimine (600 Da BPEI) and its poly(ethylene glycol) (PEG)ylated derivative (PEG-BPEI) that has a low in vivo toxicity. The mechanism of action is elucidated with fluorescence assays of drug influx and isothermal calorimetry data showing the chelation of essential Zn2+ ions. These results suggested that 600 Da BPEI and PEG-BPEI may also improve the uptake of antibiotics and ß-lactamase inhibitors. Indeed, the CRE E. coli strain is rendered susceptible to the combination of piperacillin and tazobactam. These results expand the possible utility of 600 Da BPEI potentiators, where previously we have demonstrated the ability to improve antibiotic efficacy against antibiotic resistant clinical isolates of Pseudomonas aeruginosa, Staphylococcus aureus, and Staphylococcus epidermidis.

Infant Mortality Associated With Prenatal Opioid Exposure.

Importance: Knowledge of health outcomes among opioid-exposed infants is limited, particularly for those not diagnosed with neonatal opioid withdrawal syndrome (NOWS). Objectives: To describe infant mortality among opioid-exposed infants and identify how mortality risk differs in opioid-exposed infants with and without a diagnosis of NOWS compared with infants without opioid exposure. Design, Setting, and Participants: A retrospective cohort study of maternal-infant dyads was conducted, linking health care claims with vital records for births from January 1, 2010, to December 31, 2014, with follow-up of infants until age 1 year (through 2015). Maternal-infant dyads were included if the infant was born in Texas at 22 to 43 weeks' gestational age to a woman aged 15 to 44 years insured by Texas Medicaid. Data analysis was performed from May 2019 to October 2020. Exposure: The primary exposure was prenatal opioid exposure, with infants stratified by the presence or absence of a diagnosis of NOWS during the birth hospitalization. Main Outcomes and Measures: Risk of infant mortality (death at age <365 days) was examined using Kaplan-Meier and log-rank tests. A series of logistic regression models was estimated to determine associations between prenatal opioid exposure and mortality, adjusting for maternal and neonatal characteristics and clustering infants at the maternal level to account for statistical dependence owing to multiple births during the study period. Results: Among 1 129 032 maternal-infant dyads, 7207 had prenatal opioid exposure, including 4238 diagnosed with NOWS (mean [SD] birth weight, 2851 [624] g) and 2969 not diagnosed with NOWS (mean [SD] birth weight, 2971 [639] g). Infant mortality was 20 per 1000 live births for opioid-exposed infants not diagnosed with NOWS, 11 per 1000 live births for infants with NOWS, and 6 per 1000 live births in the reference group (P < .001). After adjusting for maternal and neonatal characteristics, mortality in infants with a NOWS diagnosis was not significantly different from the reference population (odds ratio, 0.82; 95% CI, 0.58-1.14). In contrast, the odds of mortality in opioid-exposed infants not diagnosed with NOWS was 72% greater than the reference population (odds ratio, 1.72; 95% CI, 1.25-2.37). Conclusions and Relevance: In this study, opioid-exposed infants appeared to be at increased risk of mortality, and the treatments and supports provided to those diagnosed with NOWS may be protective. Interventions to support opioid-exposed maternal-infant dyads are warranted, regardless of the perceived severity of neonatal opioid withdrawal.